The Experts below are selected from a list of 962178 Experts worldwide ranked by ideXlab platform
Serena Coppolino Perfumi - One of the best experts on this subject based on the ideXlab platform.
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deindividuation effects on normative and informational Social Influence within computer mediated communication
Computers in Human Behavior, 2019Co-Authors: Serena Coppolino Perfumi, Corrado Caudek, Franco Bagnoli, Andrea GuazziniAbstract:Research on Social Influence shows that different patterns take place when this phenomenon happens within computer-mediated-communication (CMC), if compared to face-to-face interaction. Information ...
Andrea Guazzini - One of the best experts on this subject based on the ideXlab platform.
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deindividuation effects on normative and informational Social Influence within computer mediated communication
Computers in Human Behavior, 2019Co-Authors: Serena Coppolino Perfumi, Corrado Caudek, Franco Bagnoli, Andrea GuazziniAbstract:Research on Social Influence shows that different patterns take place when this phenomenon happens within computer-mediated-communication (CMC), if compared to face-to-face interaction. Information ...
Jie Tang - One of the best experts on this subject based on the ideXlab platform.
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deepinf Social Influence prediction with deep learning
arXiv: Social and Information Networks, 2018Co-Authors: Jiezhong Qiu, Jian Tang, Yuxiao Dong, Kuansan Wang, Jie TangAbstract:Social and information networking activities such as on Facebook, Twitter, WeChat, and Weibo have become an indispensable part of our everyday life, where we can easily access friends' behaviors and are in turn Influenced by them. Consequently, an effective Social Influence prediction for each user is critical for a variety of applications such as online recommendation and advertising. Conventional Social Influence prediction approaches typically design various hand-crafted rules to extract user- and network-specific features. However, their effectiveness heavily relies on the knowledge of domain experts. As a result, it is usually difficult to generalize them into different domains. Inspired by the recent success of deep neural networks in a wide range of computing applications, we design an end-to-end framework, DeepInf, to learn users' latent feature representation for predicting Social Influence. In general, DeepInf takes a user's local network as the input to a graph neural network for learning her latent Social representation. We design strategies to incorporate both network structures and user-specific features into convolutional neural and attention networks. Extensive experiments on Open Academic Graph, Twitter, Weibo, and Digg, representing different types of Social and information networks, demonstrate that the proposed end-to-end model, DeepInf, significantly outperforms traditional feature engineering-based approaches, suggesting the effectiveness of representation learning for Social applications.
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Social Influence locality for modeling retweeting behaviors
International Joint Conference on Artificial Intelligence, 2013Co-Authors: Jing Zhang, Jie Tang, Biao Liu, Ting ChenAbstract:We study an interesting phenomenon of Social Influence locality in a large microblogging network, which suggests that users' behaviors are mainly Influenced by close friends in their ego networks. We provide a formal definition for the notion of Social Influence locality and develop two instantiation functions based on pairwise Influence and structural diversity. The defined Influence locality functions have strong predictive power. Without any additional features, we can obtain a F1-score of 71.65% for predicting users' retweet behaviors by training a logistic regression classifier based on the defined functions. Our analysis also reveals several intriguing discoveries. For example, though the probability of a user retweeting a microblog is positively correlated with the number of friends who have retweeted the microblog, it is surprisingly negatively correlated with the number of connected circles that are formed by those friends.
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dynamic Social Influence analysis through time dependent factor graphs
Advances in Social Networks Analysis and Mining, 2011Co-Authors: Chi Wang, Jimeng Sun, Jie Tang, Jiawei HanAbstract:Social Influence, the phenomenon that the actions of a user can induce her/his friends to behave in a similar way, plays a key role in many (online) Social systems. For example, a company wants to market a new product through the effect of ``word of mouth'' in the Social network. It wishes to find and convince a small number of influential users to adopt the product, and the goal is to trigger a large cascade of further adoptions. Fundamentally, we need to answer the following question: how to quantify the Influence between two users in a large Social network? To address this question, we propose a pair wise factor graph (PFG) model to model the Social Influence in Social networks. An efficient algorithm is designed to learn the model and make inference. We further propose a dynamic factor graph (DFG) model to incorporate the time information. Experimental results on three different genres of data sets show that the proposed approaches can efficiently infer the dynamic Social Influence. The results are applied to the Influence maximization problem, which aims to find a small subset of nodes (users) in a Social network that could maximize the spread of Influence. Experiments show that the proposed approach can facilitate the application.
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a survey of models and algorithms for Social Influence analysis
Social Network Data Analytics, 2011Co-Authors: Jimeng Sun, Jie TangAbstract:Social Influence is the behavioral change of a person because of the perceived relationship with other people, organizations and society in general. Social Influence has been a widely accepted phenomenon in Social networks for decades. Many applications have been built based around the implicit notation of Social Influence between people, such as marketing, advertisement and recommendations. With the exponential growth of online Social network services such as Facebook and Twitter, Social Influence can for the first time be measured over a large population. In this chapter, we survey the research on Social Influence analysis with a focus on the computational aspects. First, we present statistical measurements related to Social Influence. Second, we describe the literature on Social similarity and Influences. Third, we present the research on Social Influence maximization which has many practical applications including marketing and advertisement.
Daphne De Groot - One of the best experts on this subject based on the ideXlab platform.
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Social Influence in computer mediated communication the effects of anonymity on group behavior
Personality and Social Psychology Bulletin, 2001Co-Authors: Tom Postmes, Russell Spears, Khaled Sakhel, Daphne De GrootAbstract:Two studies examined hypotheses derived from a Social Identity model of Deindividuation Effects (SIDE) as applied to Social Influence in computer-mediated communication (CMC) in groups. This model predicts that anonymity can increase Social Influence if a common group identity is salient. In a first study, group members were primed with a certain type of Social behavior (efficiency vs. proSocial norms). Consistent with the model, anonymous groups displayed prime-consistent behavior in their task solutions, whereas identifiable groups did not. This suggests that the primed norm took root in anonymous groups to a greater extent than in identifiable groups. A second study replicated this effect and showed that nonprimed group members conformed to the behavior of primed members, but only when anonymous, suggesting that the primed norm was Socially transmitted within the group. Implications for Social Influence in small groups are discussed.
Kenneth S Kurani - One of the best experts on this subject based on the ideXlab platform.
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Social Influence consumer behavior and low carbon energy transitions
Annual Review of Environment and Resources, 2012Co-Authors: Jonn Axsen, Kenneth S KuraniAbstract:Realizing a low-carbon energy future requires pervasive changes in consumer behavior. Here, we examine the role of Social Influence in transitioning toward new low-carbon products and practices. We review and critique five research perspectives of how Social interactions affect the spread of new behaviors through Social networks: diffusion of functional information across Social groups; conformity to others' behaviors; dissemination by organized, resourceful Social groups motivated to promote societal goods; translation of consumers' perceptions between Social groups; and reflexivity of individuals' continual search for self-development and expression through lifestyle practices, including their Social context and consumption. Each perspective observes different Social processes and holds different implications for policies and strategies to achieve low-carbon energy transitions. No single perspective seems adequate to characterize Social Influence. We conclude with a set of priorities to develop an integ...
